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Autonomous Surveillance of Infants' Needs Using CNN Model for Audio Cry Classification

Infants portray suggestive unique cries while sick, having belly pain, discomfort, tiredness, attention
and desire for a change of diapers among other needs. There exists limited knowledge in accessing
the infants’ needs as they only relay
information through suggestive cries. Many teenagers tend to give birth at an early
age, thereby exposing them to be the key monitors of their own babies. They
tend not to have sufficient skills in monitoring the infant’s dire needs, more so during the early stages of infant development.
Artificial intelligence has shown promising efficient predictive analytics
from supervised, and unsupervised to reinforcement learning models. This study, therefore, seeks to develop an android app that could be used to discriminate
the infant audio cries by leveraging the strength of convolution neural networks
as a classifier model. Audio analytics from many kinds of literature is an untapped area
by researchers as it’s ....

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"Predicting Credit Ratings using Deep Learning Models – An Analysis of " by Shweta Pol, Manoj hudnurkar et al.

Due to the complexity of transactions and the availability of Big Data, many banks and financial institutions are reviewing their business models. Various tasks get involved in determining the credit worthiness like working with spreadsheets, manually gathering data from customers and corporations, etc. In this research paper, we aim to automate and analyze the credit ratings of the Information and technology industry in India. Various Deep-Learning models are incorporated to predict the credit rankings from highest to lowest separately for each company to find the best fit model. Factors like Share Capital, Depreciation & Amortisation, Intangible Assets, Operating Margin, inventory valuation, etc., are the parameters that contribute to the credit rating predictions. The data collected for the study spans between the years FY-2015 to FY-2020. As per the research been carried out with efficiencies of different Deep Learning models been tested and compared, MLP gained the highest eff ....

Artificial Neural Networks , Big Data , Various Deep Learning , Share Capital , Intangible Assets , Operating Margin , Deep Learning , Indian It Industry , Big Data , Artificial Intelligence , Credit Ratings , Multi Layer Perceptron , Ovid 19 Pandemic , Deep Learning , Financial Ratios ,

Investigation of the orbital period and mass relations for W UMa-type contact systems

Abstract. New relationships between the orbital period and some parameters of W Ursae Majoris (W UMa) type systems are presented in this study. To investigate t ....

Monte Carlo , Artificial Neural Networks , Gaia Early Data Release , Markov Chain Monte Carlo , Multi Layer Perceptron ,

"PSPSO: A package for parameters selection using particle swarm optimiz" by Ali Haidar, Matthew Field et al.

"PSPSO: A package for parameters selection using particle swarm optimiz" by Ali Haidar, Matthew Field et al.
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Support Vector Machine , Multi Layer Perceptron , Extreme Gradient Boosting , Gradient Boosting Decision Trees , ஆதரவு திசையன் இயந்திரம் , தீவிர சாய்வு அதிகரிக்கும் ,

Fujitsu and Hokkaido University develop 'explainable AI' technology

Feb 8, 2021
Fujitsu Laboratories and Hokkaido University have announced the development of a new technology based on the principle of “explainable AI” that automatically presents users with steps needed to achieve a desired outcome based on AI results about data, for example, from medical checkups.
“Explainable AI” represents an area of increasing interest in the field of artificial intelligence and machine learning. While AI technologies can automatically make decisions from data, “explainable AI” also provides individual reasons for these decisions–this helps avoid the so-called “black box” phenomenon, in which AI reaches conclusions through unclear and potentially problematic means.
While certain techniques can also provide hypothetical improvements one could take when an undesirable outcome occurs for individual items, these do not provide any concrete steps to improve. ....

School Of Information Science , Arimura Laboratory , Hokkaido University , Fujitsu Laboratories , Graduate School , Information Science , Logistic Regression , Random Forest , Multi Layer Perceptron , பள்ளி ஆஃப் தகவல் அறிவியல் , ோக்கைடோ பல்கலைக்கழகம் , ஃப்யூஜிட்ஸு ஆய்வகங்கள் , பட்டதாரி பள்ளி , தகவல் அறிவியல் , லாஜிஸ்டிக் பின்னடைவு , சீரற்ற காடு ,